This proposal explores new computational methods for integrating, analyzing and visualizing the rapidly growing genomic and epigenomic information in The Cancer Genome Atlas (TCGA). In the long range these methods and their variants will enable rigorous identification of molecular biomarkers for distinguishing cancer, their subtypes, theirs stages and their outcome, providing the basis for developing improved diagnostics and prognostics. They will also enable identification of the pathways and processes that are central to the initiation and progression of tumors, and thereby inform the choice of therapeutic target selection. Until now most methods for discovering class differences related to cancer have been based on the analysis of mRNA transcription. Here we explore the modification, use and adaptation of advanced statistical methods for integrating TCGA data, and the use of our VISANT mining tool for integrating TCGA with other publicly available data. The long term objective is to develop methods that will be widely disseminated and used to discover reliable biomarkers for cancer development and progression, and to gain a deeper understanding of the key alterations that occur during transformation.